Causal inference with count regression

In this fifth post of the causal inference series, we practice with Poisson and negative-binomial models for unbounded count data. Since I’m a glutton for punishment, we practice both as frequentists and as Bayesians. You’ll find a little robust sandwich-based standard error talk, too.

Regression models for 2-timepoint non-experimental data

I recently came across Jeffrey Walker’s free text, Elements of statistical modeling for experimental biology, which contains a nice chapter on 2-timepoint experimental designs. Inspired by his work, this post aims to explore how one might analyze non-experimental 2-timepoint data within a regression model paradigm.

Bayesian robust correlations with brms (and why you should love Student’s \(t\))

In this post, we’ll show how Student’s t-distribution can produce better correlation estimates when your data have outliers. As is often the case, we’ll do so as Bayesians.

Robust Linear Regression with Student’s \(t\)-Distribution

The purpose of this post is to demonstrate the advantages of the Student’s t-distribution for regression with outliers, particularly within a Bayesian framework.